The Research Requests AI Couldn't Answer

Real Examples of What GovWin IQ Analyst Support Looks like in Practice

The pitch for AI-powered market intelligence sounds straightforward: pull your data, interpret your contracts, build your contact lists, tell you where to grow. That version works until you need to know why the Department of State's FY26 spending looks suspiciously low, or why a search for a specific task order keeps coming back empty, or whether the agency decision-maker you're about to call is still in that seat.

Deltek's Dela AI is part of GovWin IQ, but it isn't the whole platform. Behind it sits the largest team of market analysts with over 750 years of combined experience in government procurement. They handle the questions that pattern recognition can't: the ones that call for judgment, context, and sometimes a phone call to the right person. Our analysts perform more than 40,000 research requests a year to help contractors make strategic growth decisions.

Here is what some of those requests look like.

Bad Data Doesn't Announce Itself

Story: FY25 and FY26 Department of State Spending Anomalies

A client reviewing federal spending trends noticed something off: FY25 totals looked inflated, and FY26 figures looked unusually low. Before building any analysis on top of it, they needed to know whether the data was reliable.

Our analysts had already identified the issue weeks earlier through proactive FPDS monitoring. Eight Department of State contracts had each been entered with obligations equal to their ceiling value ($2.7 billion each) in FY25, inflating year totals significantly. A correction effort in FY26 then de-obligated $920 million per contract, which caused FY26 figures to drop in a way that looked suspicious without context. The government eventually issued corrections for both fiscal years, but only after a period where the data told a misleading story.

Because the team had been tracking this from the beginning, they walked the client through the full chain of events: every contract affected, what happened in each fiscal year, and why the corrected numbers looked the way they did. The client walked away with no guesswork or anxiety about whether the analysis was built on bad data.

An automated system can flag a statistical anomaly. It cannot trace corrections across fiscal years, identify which specific contracts drove the discrepancy, or explain what caused it and when it was fixed. That level of explanation requires someone who knows where to look.

Answer the Questions Your BD Team Hasn’t Thought of Yet

Story: Federal Footprint Gap Analysis for a Large Prime Contractor

Before a leadership planning meeting, a large federal prime contractor needed a clear picture of which agencies it wasn't supporting and where the best market opportunities were.

Our analyst delivered two complementary analyses. The first examined a requested set of the company's UEIs. The second looked at agency-specific NAICS activity tied to those same UEIs. That second layer was not part of the original request, but the analyst anticipated its value for determining whitespace and competitive positioning.

The result was a package ready for an executive-level BD meeting. It did not just answer the question asked. It answered the questions that were bound to come up.

A static data pull could have produced the first report. The second required someone who understood what a BD team needs to walk into a competitive gap analysis when their growth depends on it.

When the Data Doesn't Exist, We Build It

Story: OASIS+ Historical Obligation Data

Multiple clients came to us asking for comprehensive OASIS and OASIS+ obligation data, including Pools, Domains, and CLINs. These are the fields you need to do any meaningful competitive or market assessment for this vehicle.

The problem: those fields are not consistently or formally reported in the federal contract awards system.

Rather than returning incomplete results or flagging the request as unanswerable, our analyst manually coded and validated those fields using GSA reporting, cross-checking against historical reports to ensure full coverage where live reporting is inconsistent. They also incorporated HCATS and FSSI BMO obligations (legacy vehicles that GSA confirmed would roll into OASIS+), because omitting them would have produced a materially incomplete picture of the vehicle's scope.

Clients need this data to assess market size, understand past performance, and evaluate their competitive position for future pursuits. Getting it wrong leads to incorrect pursuit decisions or flawed proposal strategies.

A tool dependent on structured, consistently reported federal data would have returned exactly what is in the system. As procurement practices change over time, inconsistencies pose risks to contractors who act without the full picture in a market as competitive as this.

Knowing the Limits of the Data Is Half the Job

Story: GSA MAS SIN-Level Reporting

A mid-size government services firm needed GSA MAS obligation data broken down by SIN. They had already tried pulling it themselves, running into walls with data that behaved in ways they could not explain, and were not sure whether their interpretation was accurate.

Our analyst's first move was not to produce a report. It was to explain what was going on.

GSA MAS has structural reporting limitations that make SIN-level obligation data inconsistently available. Rather than forcing false precision where the data did not support it, our analyst delivered a scoped report with clear guidance on what the data could and could not reliably show, including worked examples that illustrated the most common misinterpretations.

The client's response: "Tell the analysts thank you so much!! You saved us countless hours of scratching our heads."

Knowing the limits of the data is just as important as knowing the data. A tool in this situation either returns incomplete data without explanation or manufactures a level of precision the underlying data does not support. Our analyst delivered output that the client could trust and speak to with confidence.

Contact Lists Are Only Useful If the People Are Still There

Story: Federal CIO and Chief AI Officer Contact List

A client needed a comprehensive, ready-to-use contact list of every federal agency CIO and Chief AI Officer ahead of a time-sensitive outreach campaign. They had already tried ChatGPT. The results came back with names but sparse contact details, and for the two specific individuals they needed most, the AI tool returned no usable information at all.

Our analyst started with a filtered pull from GovWin's Federal Contacts database: CIO/IT Office contact type, Agency Senior Leadership role type, limited to contacts on verified org charts. That foundation produced more than 200 federal IT leadership contacts. From there, the analyst cross-checked across multiple current government and industry tracking sources, identified approximately 20 profiles that needed updates, and validated the accuracy of roughly 100 leaders' current roles.

Both individuals the client had flagged as missing were already in the GovWin database with verified phone numbers and email addresses.

The analyst also ensured the dataset incorporated sources as recent as February 2026 to capture newly designated CAIOs, a role that is both newly mandated and actively evolving. A model trained on static public data cannot account for that.

The client received a vetted, ready-to-use contact list. Not a starting point for further research.

Org Chart Data You Can Stake Outreach Decisions On

Story: Air Force Life Cycle Management Center Org Chart Validation

A client needed to confirm the current organizational structure of the Air Force Life Cycle Management Center's Cyber and Networks Directorate. The real need was more specific: they needed to know that the leadership, reporting relationships, and points of contact in that org chart were accurate enough to stake outreach decisions on.

Our analyst did not just confirm what was already in the system; they expanded it, adding 10 recently verified leadership profiles. They cross-referenced GovWin records against AFLCMC's official January 2026 org chart to ensure alignment. Where information could not be confirmed, it was excluded rather than filled in with a best guess.

They also explained to the client how GovWin maintains and updates org charts over time, so they could understand not just who to contact today, but how the data stays current as the DoD environment changes.

A scraper could have returned a static chart or surfaced names from public sources. It would not have exercised judgment about which information was verified enough to act on. Knowing what to include, what to exclude, and how to explain the difference is what transformed a data request into a tool the client could use.

The Thread Running Through Every Request

Across every one of these stories, the sequence is the same: a client has a question, the data does not quite answer it on its own, and what bridges the gap is an analyst who understands federal procurement well enough to know what the data means, where it falls short, and how to fill in what is missing responsibly.

AI surfaces data. Our analysts make it usable.

That is not a positioning statement. It is what happened, in real tickets, with real clients, over and over again.

GovWin IQ offers research support requests for a variety of topics, including but not limited to: data pulls, org chart validation, agency outreach, vendor analysis, contract research, anonymous question submittal to the government, and much more.

Talk to an expert to learn more